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computer science, computer engineering, & Applied Computing (CSCE), Congress in

作者： Vinicius D. Do Nascimento Tiago A. O. Alves Diego L. C. Dutra Sandip Kundu Systems Engineering and Computer Science Program (PESC) Federal University of Rio de Janeiro (UFRJ) Rio de Janeiro Brazil Mathematics and Statistics Institute State University of Rio de Janeiro (UERJ) Rio de Janeiro Brazil Electrical and Computer Engineering (ECE) Department University of Massachusetts Amherst USA

Maritime authorities (MA) must track fishing vessels to ensure that fishing activities are limited to permitted areas. If illegal fishing is suspected, resources must be allocated to intercept and inspect the vessels. Thus, a false flag by the MA is costly, so it is important to use accurate detection methods. We compare the accuracy and computational time of the main approaches for detecting fishing activities described in the literature, using the Global Fishing Watch (GFW) dataset. We find that Long Short-Term Memory (LSTM) neural networks achieves an optimal accuracy of 1.00, while the random forest approach comes second with an accuracy of 0.87. Given the high cost of mistakes for MA, we conclude that the LSTM's high computational cost is worthwhile.

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Generative AI Impact on the Future of Work: Insights from Software Development

Generative AI Impact on the Future of Work: Insights from So...

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IEEE International Conference on systems, Man and Cybernetics

作者： Caroline Da Conceição Lima Herbert Salazar Yuri Lima Carlos Eduardo Barbosa Matheus Argôlo Alan Lyra Jano Moreira de Souza Program of Systems and Computer Engineering (PESC) COPPE Universidade Federal do Rio de Janeiro (UFRJ) Rio de Janeiro - RJ Brazil Centro de Analises de Sistemas Navais Marinha do Brasil Praca Barao de Ladario - Ilha das Cobras Rio de Janeiro - RJ Brazil

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Recent Artificial Intelligence advancements raise concerns about the future of work, particularly technological unemployment. Studies show automation's impact, but tools like ChatGPT disrupt even traditionally secure professions like programming. In this study, we reevaluate AI's effects using a method to assess the impact of Generative AI technologies on occupations to understand the potential effects of Generative AI systems on software development work. Valuable insights were obtained by gathering the view of a group of workers, primarily composed of developers who are starting their careers, regarding the impact of these technologies on the tasks they perform to provide a comprehensive understanding of the implications of Generative AI for software development. Results show that all programming tasks performed by these workers would experience some impact by Generative AI -65% of the tasks being considerably impacted, 12% moderately impacted, and 18% minimally impacted. This analysis highlights the substantial influence of Generative AI technologies on software development, mainly affecting those in the early stages of their career. The results of this work contribute to the academic community with valuable information. Policymakers can also use this information, as this work provides a comprehensive view of the impacts of Generative AI on software developers, considering their direct impact on job tasks.

关键词： Automation Generative AI engineering profession Chatbots Software Unemployment programming profession Cybernetics Software development management

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Extracting Semantic Information from Patent Claims Using Phrasal Structure Annotations

Extracting Semantic Information from Patent Claims Using Phr...

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Brazilian Conference on Intelligent systems (BRACIS)

作者： Danilo Silva de Carvalho Felipe Maia Galvão França Priscila Machado Vieira Lima Systems Engineering and Computer Science Program-COPPE Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil Instituto Tércio Pacitti (NCE) Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil

The rapid change of trading values from tangible assets to Intelectual Property has put both businesses and academia in a race to acquire and protect the rights to exploit such property. This is mainly accomplished in the form of patent issuing by the governments, being time consuming and complicated due to the vast amount of documents that need to be analyzed in order to assert the novelty or validity of a patent application. Patent information retrieval research is thus growing quickly to support document analysis across multiple domains and information systems. One of the big challenges in patent analysis is the identification of the elements of innovation (concepts, processes, materials) and the relations between them, in the patent text. This paper presents a method for extracting semantic information from patent claims by using semantic annotations on phrasal structures, abstracting domain ontology information and outputting ontology-friendly structures to achieve generalization. An extraction system built upon the method is briefly evaluated on a document sample from INPI, the Brazilian patent office, a challenging information source.

关键词： Semantics Patents Syntactics Data mining Training Ontologies Testing

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An agent-based architecture for knowledge management in context-aware business processes

An agent-based architecture for knowledge management in cont...

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International Conference on computer Supported Cooperative Work in Design

作者： Leonardo A. de C. Gatti Flávia Maria Santoro Vanessa T. Nunes NP2TEC - Research and Practice Group in Information Technology UNIRIO Rio de Janeiro Brazil NP2TEC-Research and Practice Group in Information Technology Department of Applied Informatics UNIRIO Federal University of Rio de Janeiro Rio de Janeiro Brazil COPPE - Systems Engineering and Computer Science Program UFRJ Rio de Janeiro Brazil

The main knowledge management challenges are to capture, store and reuse contextual knowledge generated during interactions that occur daily in an organization. In this paper, we propose an activity context-aware architecture to support knowledge management in working processes. The required features for this architecture are processing, reasoning and sharing contextual knowledge involving information about activities performed. We also present results from evaluation of our proposal — A-CoBrA — for a specific domain.

关键词： Knowledge management computer architecture Context modeling Collaborative work Proposals Information technology Informatics Service oriented architecture Ontologies Knowledge engineering

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Toward a computer-Supported Remote Serendipity: Understanding the Role of Collaboration in Remote Innovation

Toward a Computer-Supported Remote Serendipity: Understandin...

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IEEE International Conference on systems, Man and Cybernetics

作者： Aline Coutinho Carlos Eduardo Barbosa Marcos Antonio De Almeida Jano Moreira De Souza Program of Systems and Computer Engineering (PESC) COPPE Universidade Federal do Rio de Janeiro (UFRJ) Rio de Janeiro RJ Brazil Centro de Análises de Sistemas Navais Marinha do Brasil Praça Barão de Ladário - Ilha das Cobras Rio de Janeiro RJ Brazil

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Since 2016, the demand for remote work has grown by nearly 400%, with 3.5 million remote job vacancies posted, and it is expected to continue to grow in the coming years. Therefore, one of the challenges in advancing remote work is fostering innovation, particularly serendipity, in a remote workforce. This fortunate discovery can pave the way for technological advancements, new business strategies, or even scientific revolutions. However, creating moments of serendipity in a remote work environment is a significant challenge. Thus, finding the right approach to stimulate serendipity in a remote work environment is an ever-evolving challenge. Therefore, this work aims to understand the possibilities of serendipity that collaboration tools used in remote work can support. The methodology used for this work was a Rapid Review. First, we explore the factors related to serendipity in physical offices, for which we identified twenty-three elements. Next, we compiled 38 strategies for remote work that were found to promote serendipity, which we organized in a framework for better observation. Our findings can serve as a starting point for designing new tools and identifying existing software and tools that already play a supportive role.

关键词： Technological innovation Reviews Pandemics Employment Virtual environments Solids Remote working Software Object recognition Guidelines

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PatternLab: From mass spectra to label-free differential shotgun proteomics

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Current Protocols in Bioinformatics 2012年第SUPPL.40期40卷

作者： Carvalho, Paulo C. Fischer, Juliana S.G. Xu, Tao Yates III, John R. Barbosa, Valmir C. Carlos Chagas Institute-Fiocruz Parańa Brazil Department of Cell Biology The Scripps Research Institute La Jolla CA United States Systems Engineering and Computer Science Program COPPE Federal University of Rio de Janeiro Brazil

PatternLab for proteomics is a self-contained computational environment for analyzing shotgun proteomic data. Recent improvements incorporate modules to facilitate the computational analysis, such as FastaDBXtractor for sequence database preparation and ProLuCID runner for simplifying and managing the protein identification search engine;modules for pushing the limits on proteomics standards, such as SEPro, which relies on a semi-labeled decoy approach for increasing confidence in filtering and organizing peptide spectrum matches;and modules with novel features, such as SEProQ for enabling labelfree quantitation by extracted ion chromatograms according to a distributed normalized ion abundance factor approach (dNIAF). Existing modules were also improved, such as the TFold module for pinpointing differentially expressed proteins. These new modules are integrated into the previously described arsenal of tools for further data analysis. Here we provide detailed instructions for operating and understanding them. © 2012 by John Wiley & Sons, Inc.

关键词： Dniaf filtering PSMs Protein identification Quantitative proteomics Semi-labeled decoy approach

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Sherlock: Capturing Probe Requests for Automatic Presence Detection

Sherlock: Capturing Probe Requests for Automatic Presence De...

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International Conference on computer Supported Cooperative Work in Design

作者： Luiz Oliveira João Henrique Daniel Schneider Jano de Souza Sérgio Rodriques Weiming Sherr IFRJ Federal Institute of Rio de Janeiro Brazil Systems Engineering and Computer Science Program/COPPE Federal University of Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research (NCE) Federal University of Rio de Janeiro Brazil National Research Council Canada Constructicn Research Centre Canada

The ubiquity of smartphones creates great opportunities for participatory sensing, where people can implicitly contribute observations about their local environments through sensors such as cameras and accelerometers. The collected data can then be aggregated and used to benefit the crowd in some way. In this paper, we report on the current development of Sherlock, a device capable of automatically detecting the presence of people in localities through evidences left by smartphones called probe requests, without any user intervention. To validate the proposed mechanism implemented in the device, we performed an experiment with ten participants in six rounds where it was possible to automatically detect 41 presence events, of which 66% could be detected within less than 30s. The implicit crowdsourcing mechanism behind this approach may allow real-time monitoring of people flows in public environments, which can enable, among other things, energy systems to be automatically orchestrated according to demand, reducing associated costs.

关键词： Probes Wireless fidelity Smart phones Wireless networks Monitoring Prototypes

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Games in Technology Forecasting & Foresight: A Rapid Review

Games in Technology Forecasting & Foresight: A Rapid Review

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IEEE International Conference on systems, Man and Cybernetics

作者： Rafael Machado Andrade Aline Lima de Souza Carlos Eduardo Barbosa Alan Lyra Herbert Salazar Yuri Lima Matheus Argôlo Jano Moreira de Souza Program of Systems and Computer Engineering (PESC) COPPE Universidade Federal do Rio de Janeiro (UFRJ) Horácio Macedo St. Rio de Janeiro RJ Brazil Centro de Análises de Sistemas Navais Marinha do Brasil Praça Barão de Ladário - Ilha das Cobras Rio de Janeiro RJ Brazil

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Future-related studies are key in identifying and predicting emerging technologies, fostering innovation, and driving scientific progress. Games have increasingly served as a tool to engage researchers in Technology Forecasting and Foresight activities, presenting a gamified approach to gather, analyze, and synthesize information to allow future foresight. In this work, we use the Rapid Review methodology to analyze the scientific literature and explore the use of games as tools for Technology Forecasting and Foresight. We analyzed and categorized the articles by theme and type of study, identifying the areas where games can be most beneficial to guide professionals performing Technology Forecasting and Foresight, helping companies and governments better understand the future. The empirical evidence found provides a comprehensive understanding of how games can enhance foresight activities and contribute to the success of Technology Forecasting and Foresight initiatives. Our analysis also provides insights for future research about gamified strategies in Foresight.

关键词： Technological innovation Reviews Databases Decision making Games Strategic planning Technology forecasting Market research Teamwork Sustainable development

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A multi-protease, multi-dissociation, bottom-up-to-top-down proteomic view of the Loxosceles intermedia venom

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Scientific Data 2017年第1期4卷 170090-170096页

作者： Trevisan-Silva, Dilza Bednaski, Aline V. Fischer, Juliana S.G. Veiga, Silvio S. Bandeira, Nuno Guthals, Adrian Marchini, Fabricio K. Leprevost, Felipe V. Barbosa, Valmir C. Senff-Ribeiro, Andrea Carvalho, Paulo C. Department of Cell Biology Federal University of Paraná Curitiba 81531-980 Brazil Computational Mass Spectrometry and Proteomics Group Carlos Chagas Institute Fiocruz Curitiba 81.350-010 Brazil Center for Computational Mass Spectrometry University of California San Diego 92093-0404 United States Functional Genomics Laboratory Carlos Chagas Institute Fiocruz Curitiba 81.350-010 Brazil Mass Spectrometry Facility RPT02H Carlos Chagas Institute Fiocruz Curitiba 81.350-010 Brazil Systems Engineering and Computer Science Program COPPE Federal University of Rio de Janeiro Rio de Janeiro 21941-914 Brazil Laboratory of Toxinology Fiocruz Rio de Janeiro 21040-900 Brazil

Venoms are a rich source for the discovery of molecules with biotechnological applications, but their analysis is challenging even for state-of-the-art proteomics. Here we report on a large-scale proteomic assessment of the venom of Loxosceles intermedia, the so-called brown spider. Venom was extracted from 200 spiders and fractioned into two aliquots relative to a 10 kDa cutoff mass. Each of these was further fractioned and digested with trypsin (4 h), trypsin (18 h), pepsin (18 h), and chymotrypsin (18 h), then analyzed by MudPIT on an LTQ-Orbitrap XL ETD mass spectrometer fragmenting precursors by CID, HCD, and ETD. Aliquots of undigested samples were also analyzed. Our experimental design allowed us to apply spectral networks, thus enabling us to obtain meta-contig assemblies, and consequently de novo sequencing of practically complete proteins, culminating in a deep proteome assessment of the venom. Data are available via ProteomeXchange, with identifier PXD005523.

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Corrigendum: A review of trends and drivers of greenhouse gas emissions by sector from 1990 to 2018 (2021 Environ. Res. Lett. 16 073005)

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Environmental Research Letters 2022年第4期17卷 049502-049502页

作者： William F Lamb Thomas Wiedmann Julia Pongratz Robbie Andrew Monica Crippa Jos G J Olivier Dominik Wiedenhofer Giulio Mattioli Alaa Al Khourdajie Jo House Shonali Pachauri Maria Figueroa Yamina Saheb Raphael Slade Klaus Hubacek Laixiang Sun Suzana Kahn Ribeiro Smail Khennas Stephane de la Rue du Can Lazarus Chapungu Steven J Davis Igor Bashmakov Hancheng Dai Shobhakar Dhakal Xianchun Tan Yong Geng Baihe Gu Jan Minx Mercator Research Institute on Global Commons and Climate Change Torgauer Straße 12–15 4 EUREF Campus #19 10829 Berlin Germany School of Earth and Environment University of Leeds LS2 9JT Leeds United Kingdom Sustainability Assessment Program School of Civil and Environmental Engineering UNSW Sydney Sydney Australia Max Planck Institute for Meteorology Bundesstrasse 53 20146 Hamburg Germany Department of Geography Ludwig-Maximilians-Universität Munich Luisenstrasse 37 80333 Munich Germany CICERO Center for International Climate Research Oslo Norway European Commission Joint Research Centre Ispra VA Italy PBL Netherlands Environmental Assessment Agency Den Haag The Netherlands Institute of Social Ecology University of Natural Resources and Life Sciences Schottenfeldgasse 29 1070 Vienna Austria Department of Transport Planning TU Dortmund University August-Schmidt-Straße 10 44227 Dortmund Germany Centre for Environmental Policy Imperial College London London United Kingdom School of Geographical Sciences University of Bristol University Road BS8 1SS Bristol United Kingdom International Institute for Applied Systems Analysis (IIASA) Schlossplatz 1 2361 Laxenburg Austria Department of Management Society and Communication Copenhagen Business School Copenhagen Denmark Openexp 17 Bd Lefebvre 75015 Paris France Integrated Research for Energy Environment and Society University of Groningen 9747AG Groningen The Netherlands Department of Geographical Sciences University of Maryland College Park MD 20742 United States of America School of Finance and Management SOAS University of London WC1H 0XG London United Kingdom Institute of Blue and Green Development Weihai Institute of Interdisciplinary Research Shandong University Weihai 264209 People’s Republic of China Federal University of Rio de Janeiro (Universidade Federal do Rio de Janeiro—UFRJ) Transport Engineering Programme (Programa de Engenharia de Transportes—PET) COPPE-UFRJ Rio de Janeiro Brazil Energy

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